Papers › UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation

UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation

24 Nov 2021CVPR 2022 1arXiv:2111.12580archive 2025-07-28

Taeyeop Lee, Byeong-Uk Lee, Inkyu Shin, Jaesung Choe, Ukcheol Shin, In So Kweon, Kuk-Jin Yoon

Learning to estimate object pose often requires ground-truth (GT) labels, such as CAD model and absolute-scale object pose, which is expensive and laborious to obtain in the real world. To tackle this problem, we propose an unsupervised domain adaptation (UDA) for category-level object pose estimation, called UDA-COPE. Inspired by recent multi-modal UDA techniques, the proposed method exploits a teacher-student self-supervised learning scheme to train a pose estimation network without using target domain pose labels. We also introduce a bidirectional filtering method between the predicted normalized object coordinate space (NOCS) map and observed point cloud, to not only make our teacher network more robust to the target domain but also to provide more reliable pseudo labels for the student network training. Extensive experimental results demonstrate the effectiveness of our proposed method both quantitatively and qualitatively. Notably, without leveraging target-domain GT labels, our proposed method achieved comparable or sometimes superior performance to existing methods that depend on the GT labels.

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Tasks

6D Pose Estimation using RGBD Domain Adaptation Object Pose Estimation Self-Supervised Learning Unsupervised Domain Adaptation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
6D Pose Estimation using RGBD	REAL275	UDA-COPE	mAP 10, 2cm	56.9	#5 of 11	Archive leaderboard	report
6D Pose Estimation using RGBD	REAL275	UDA-COPE	mAP 10, 5cm	66.0	#5 of 11	Archive leaderboard	report
6D Pose Estimation using RGBD	REAL275	UDA-COPE	mAP 3DIou@50	82.6	#5 of 11	Archive leaderboard	report
6D Pose Estimation using RGBD	REAL275	UDA-COPE	mAP 3DIou@75	62.5	#5 of 11	Archive leaderboard	report
6D Pose Estimation using RGBD	REAL275	UDA-COPE	mAP 5, 2cm	30.4	#5 of 11	Archive leaderboard	report
6D Pose Estimation using RGBD	REAL275	UDA-COPE	mAP 5, 5cm	34.8	#5 of 11	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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